senses_valence <- read_csv("data/senses_valence.csv") %>%
filter(
...
)QML tutorial - Week 5
1 Emotional valence of sense words
We will model data from Winter, 2016. Taste and smell words form an affectively loaded part of the English lexicon. DOI: 10.1080/23273798.2016.1193619. The study looked at the emotional valence of sense words in English, in the domains of taste and smell.
To download the file with the data right-click on the following link and download the file: senses_valence.csv. (Note that tutorial files are also linked in Course content). Remember to save the file in data/ in the course project folder.
Create a new .Rmd file first, save it in code/ and name it tutorial-w05 (the extension .Rmd is added automatically!).
I leave to you creating title headings in your file as you please. Remember to to add knitr::opts_knit$set(root.dir = here::here()) in the setup chunk and to attach the tidyverse.
Let’s read the data. The original data also include words from other senses, so for this tutorial we will filter the data to include only the following Modality: Smell and Taste words. Complete the code below to filter the data.
This is what the data frame looks like.
senses_valenceThere are three columns:
Word: the English word [categorical].Modality:TasteorSmell[categorical].Val: emotional valence [numeric, continuous]. The higher the number the more positive the valence.
Let’s quickly check the minimum and maximum values in Val. Do you remember which function returns those two?
...(senses_valence$Val)[1] 4.630476 6.436000This is what the density plot of Val looks like.
senses_valence %>%
ggplot(aes(Val)) +
geom_density(fill = "gray", alpha = 0.5) +
geom_rug()
And if we separate by Modality, we can see that the density of taste words is shifted towards higher values than that of smell words. Try to reproduce the following plot in your code.
If you were asked to write a description of the data, you could write the following:
The data contains 72 observations of the emotional valence of taste (N = 47) and smell (N = 25) words. The range of emotional valence is 1.8 (min = 4.6, max = 6.4). The median valence for taste words is 5.9 (SD = 0.3) while the median valence for smell words is 5.5 (SD = 0.3). As shown in the plot, in this sample of 72 words, taste words are generally associated with a somewhat higher emotional valence than smell words.
1.1 Modelling emotional valence
Now that we got an overview of what the data looks like, let’s move onto modelling it with brm()!
Let’s assume, as we did with the VOT values, that the emotional valence values come from a Gaussian distribution.1 In notation:
\[ \text{val} \sim Gaussian(\mu, \sigma) \]
Now, we want to estimate \(\mu\) so that we take into consideration whether the modality is “smell” or “taste”. In other words, we want to model valence as a function of modality, in R code Val ~ Modality.
Modality is a categorical variable (a chr character column in sense_valence) so it is coded using the default treatment coding system (this is done under the hood by R for you!). Since Modality has 2 levels, R will create for us a N-1 = 1 dummy variable (for illustration, we can imagine that it’s called modality_taste).
This is what the coding would look like:
| modality_taste | |
|---|---|
| modality = smell | 0 |
| modality = taste | 1 |
Now we allow \(\mu\) to vary depending on modality.
\[ \mu = \beta_0 + \beta_1 \cdot modality_{Taste} \]
Let’s unpack the equation. There’s a bit of algebra in what follows, so take your time if this is a bit out of your comfort zone. But it’s worth it—familiarity with basic algebra will also help you become more comfortable working with linear models.
- \(\beta_0\) is the mean valence \(\mu\) when [modality = smell]. That’s because the variable \(modality_{Taste}\) takes on the value 0 when [modality = smell] (as we can see from the table above). Multiplying by 0 means that \(\beta_1\) vanishes from the equation, and all that’s left is \(\beta_0\).
\[ \begin{aligned} \mu &= \beta_0 + \beta_1 \cdot modality_{Taste}\\ \mu_{mod = smell} &= \beta_0 + \beta_1 \cdot 0 \\ \mu_{mod = smell} &= \beta_0 \\ \end{aligned} \]
- \(\beta_1\) is the difference in mean valence \(\mu\) between the mean valence when [modality = taste] and the mean valence when [modality = smell]. That’s because \(modality_{Taste}\) takes on the value 1 when [modality = taste], and multiplying \(\beta_1\) by 1 leaves it unchanged in the equation.
\[ \begin{aligned} \mu &= \beta_0 + \beta_1 \cdot modality_{Taste}\\ \mu_{mod = taste} &= \beta_0 + \beta_1 \cdot 1 \\ \mu_{mod = taste} &= \beta_0 + \beta_1 \\ \end{aligned} \]
- How do we know that \(\beta_1\) really represents the difference between the mean valence when [modality = smell] and the mean valence when [modality = taste]? Remember that \(\mu_{mod = smell} = \beta_0\), as we said in the first point above. We can substitute this into the equation from the last point, and then isolate \(\beta_1\) step by step as follows:
\[ \begin{aligned} \beta_0 & = \mu_{mod=smell} \\ \mu_{mod = taste} &= \beta_0 + \beta_1 \\ \mu_{mod = taste} &= \mu_{mod=smell} + \beta_1 \\ \mu_{mod = taste} - \mu_{mod=smell} &= \mu_{mod=smell} + \beta_1 - \mu_{mod=smell} \\ \mu_{mod = taste} - \mu_{mod=smell} &= \beta_1 \end{aligned} \]
So, \(\beta_1\) really is the difference between the means of these two levels of Modality.
Now we can define the probability distributions of \(\beta_0\) and \(\beta_1\).
\[ \begin{align} \beta_0 & \sim Gaussian(\mu_0, \sigma_0) \\ \beta_1 & \sim Gaussian(\mu_1, \sigma_1) \end{align} \]
And the usual for \(\sigma\).
\[ \sigma \sim TruncGaussian(\mu_2, \sigma_2) \]
Here’s the full mathematical specification of the model.
\[ \begin{align} \text{val} & \sim Gaussian(\mu, \sigma) \\ \mu & = \beta_0 + \beta_1 \cdot modality_{Taste} \\ \beta_0 & \sim Gaussian(\mu_0, \sigma_0) \\ \beta_1 & \sim Gaussian(\mu_1, \sigma_1) \\ \sigma & \sim TruncGaussian(\mu_2, \sigma_2) \end{align} \]
All together, we need to estimate the following parameters: \(\mu_0, \sigma_0, \mu_1, \sigma_1, \mu_2, \sigma_2\).
1.2 Run the model
Now we know what the model will do, we can run it.
Before that though, create a folder called cache/ in the data/ folder of the RStudio project of the course. We will use this folder to save the output of model fitting so that you don’t have to refit the model every time. (This is useful because as models get more and more complex, they can take quite a while to fit.)
After you have created the folder, run the following code.
val_bm <- brm(
Val ~ Modality,
family = gaussian(),
data = senses_valence,
backend = "cmdstanr",
# Save model output to file
file = "data/cache/val_bm"
)The model will be fitted and saved in data/cache/ with the file name val_bm.rds. If you now re-run the same code again, you will notice that brm() does not fit the model again, but rather reads it from the file (no output is shown, but trust me, it works! Check the contents of data/cache/ to see for yourself.).
When reporting the model specification, you can write the following:
We fitted a Bayesian model with emotional valency as the outcome variable and modality (smell vs taste) as the only predictor. A Gaussian family was used as the outcome distribution family. The modality predictor was coded with the default treatment contrasts (with “smell” as the reference level).
1.3 Interpret the model output
Let’s inspect the model summary.
summary(val_bm) Family: gaussian
Links: mu = identity; sigma = identity
Formula: Val ~ Modality
Data: senses_valence (Number of observations: 72)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 5.47 0.06 5.35 5.59 1.00 3800 3149
ModalityTaste 0.34 0.08 0.18 0.50 1.00 3866 2984
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 0.32 0.03 0.27 0.38 1.00 3136 2573
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).Model summaries have three main parts:
Information on the model, like family, formula and data.
Population-level effects, with estimates of the intercept and any predictor’s coefficient.
Family specific parameters, with
sigmawhen using a Gaussian family.
Look at the Population-Level Effects part of the summary.
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 5.47 0.06 5.35 5.59 1.00 3800 3149
ModalityTaste 0.34 0.08 0.18 0.50 1.00 3866 2984We get two coefficients:
Intercept: this is our \(\beta_0\).ModalityTaste: this is our \(\beta_1\).
Each coefficient has an Estimate and an Est.Error (estimate error). As we have seen last week, these are the mean and SD of the probability distribution of the coefficients.
In notation:
\[ \begin{align} \mu & = \beta_0 + \beta_1 \cdot modality_{Taste} \\ \beta_0 & \sim Gaussian(5.47, 0.06) \\ \beta_1 & \sim Gaussian(0.34, 0.08) \end{align} \]
This means that:
The probability distribution of the mean valence when [modality = smell] is a Gaussian distribution with mean = 5.47 and SD = 0.06.
The probability distribution of the difference between the mean valence of [modality = taste] and that of [modality = smell] is a Gaussian distribution with mean = 0.34 and SD = 0.08.
Now let’s look at the Credible Intervals (CrIs, or CI in the model summary) of the coefficients. Based on the reported 95% CrIs, we can say that:
We can be 95% confident that (you can also say, “there is a 95% probability that”), based on the data and the model, the mean emotional valence of smell words is between 5.35 and 5.59.
There is a 95% probability that the difference in mean emotional valence between taste and smell words is between 0.18 and 0.5. In other words, the emotional valence increases by 0.18 to 0.5 in taste words relative to smell words.
You could report these results in the following way:
According to the model, the mean valency of smell words is 5.47 (SD = 0.06), with a 95% probability that it is between 5.35 and 5.59. At 95% confidence, the difference in valency between taste and smell words is between 0.18 and 0.5 (\(\beta\) = 0.34, SD = 0.08).
But what about the probability distribution of the valence of taste words?
For that we need to do a bit of data wrangling, but before we get there we need to talk about posterior probability distributions and posterior draws.
2 Posteriors
Note that you can abbreviate the phrase “posterior probability distribution” to posterior probability or just posterior, but remember we are talking about probability distributions.
They are called posterior probabilities because they are the product of the evidence from the data and any prior probability distribution. A prior probability distribution is a probability distribution that conveys the prior beliefs of the researcher in regards to the model coefficients.
We will not further talk about priors in this course, but for now, just know that we are using the default priors used by brm(): these priors are basically equivalent to saying “I have no prior beliefs about the model coefficients, so I will let the data speak for themselves”.
If you are interested in learning a bit more about priors, check the “Extra” box below.
But how are these posterior probabilities constructed, you might ask?
The answer is: through the MCMC algorithm! (If you need a refresher on what MCMC is, check the tutorial from Week 4).
2.1 Posterior draws
In order to estimate the coefficients, brm() runs a number of MCMC iterations and at each iteration a value for each coefficient in the model is proposed (this is a simplification, if you want to know more about the inner working of MCMCs, check the Statistical (Re)Thinking book).
At the end, what you get is a list of values for each coefficient in the model. These are called the posterior draws. The probability distribution of the posterior draws of a coefficient is the posterior probability distribution of that coefficient.
If you take the mean and standard deviation of the posterior draws of a coefficient, you get the Estimate and Est.Error values in the model output! Let’s try this.
You can easily extract the posterior draws of all the model’s coefficients with the as_draws_df() function from the brms package.
val_bm_draws <- as_draws_df(val_bm)
val_bm_drawsCheck the first three columns of the tibble (don’t worry about the other columns—those are technical things that are important for brms, but not so important for us). The first three columns are our \(\beta_0\), \(\beta_1\) and \(\sigma\) from the model formulae above!
b_Intercept: \(\beta_0\).b_ModalityTaste: \(\beta_1\).sigma: \(\sigma\).
(Why is there a b at the beginning of b_Intercept and b_ModalityTaste? Well, “b” is for “beta”, and it’s brms’s way of telling us what kind of coefficient we’re working with.)
Now go ahead and use summarise() (careful, not the summary() function) to get the mean and standard deviation of the posteriors of the three coefficients.
val_bm_draws %>%
summarise(
...
)Now that we have the posterior draws in val_bm_draws, we can simply use ggplot2 to plot the probability density of the draws, a.k.a., the posterior probability distribution, for each coefficient.
2.2 Plotting probability distributions
They say a plot is better than a thousand words, so why don’t we plot the probability distributions of the Intercept (\(\beta_0\)) and ModalityTaste (\(\beta_1\)) coefficients?
There are different methods for plotting these, each of which has its own advantages and disadvantages. Some methods require extra packages, while others work with the packages you already know of.
To simplify things, we will use a method that works out of the box using just ggplot2.
Let’s start with the posterior probability, or posterior for short, of b_Intercept.
val_bm_draws %>%
ggplot(aes(b_Intercept)) +
geom_density(fill = "gray", alpha = 0.5) +
labs(
title = "Posterior probability distribution of Intercept"
)
Nice, huh?
Now with b_ModalityTaste. We can add a vertical line that shows the mean using geom_vline().
taste_mean <- mean(val_bm_draws$b_ModalityTaste)
val_bm_draws %>%
ggplot(aes(b_ModalityTaste)) +
geom_density(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = taste_mean, linetype = "dashed", colour = "purple") +
labs(
title = "Posterior probability distribution of ModalityTaste"
)
Now recreate the following plot, which shows the posterior distribution of sigma.
2.3 Conditional probability distributions
This is all great, but as we asked ourselves above, what if you wanted to know the probability distribution of mean emotional valence in either smell or taste words?
So far, we only know the probability distribution of mean valence for smell words (\(\beta_0\)), and how that valence is changed for taste words (\(\beta_1\)) relative to smell words. We don’t yet know the probability distribution of mean emotional valence for taste words. How do we find that distribution?
This is where conditional posterior probability distributions (or conditional probabilities for short) come in!
In the val_bm model we had only included one predictor (we will see how to include more in the coming weeks): Modality, which has two levels Smell and Taste.
How do we obtain the conditional probabilities of emotional valence for smell and taste words respectively?
The formula of \(\mu\) above can help us solve the mystery.
\[ \mu = \beta_0 + \beta_1 \cdot modality_{Taste} \]
The conditional probability of mean emotional valence in smell words is just \(\beta_0\). Why? Remember that when [modality = smell], \(modality_{Taste}\) is 0, so:
\[ \beta_0 + \beta_1 \cdot 0 = \beta_0 \]
In other words, the conditional probability of mean emotional valence in smell words is equal to the posterior probability of b_Intercept. This is so because Modality is coded using the treatment coding system and Smell is the reference level (nothing mysterious here).
But what about taste words? Here’s where we need to do some more maths/data processing.
When [modality = taste], \(modality_{Taste}\) is 1, so:
\[ \beta_0 + \beta_1 \cdot 1 = \beta_0 + \beta_1 \]
So to get the conditional posterior probability of mean emotional valence for taste words, we need to sum the posterior draws of b_Intercept (\(\beta_0\)) and b_ModalityTaste (\(\beta_1\)): \(\beta_0 + \beta_1\).
It couldn’t be easier than using the mutate() function from dplyr. Remember that mutate() creates a new column (here, called taste) based on the values of other columns. Here, we’re just adding together the values in b_Intercept and those in b_ModalityTaste (a.k.a., the posterior draws for each of those coefficients).
val_bm_draws <- val_bm_draws %>%
mutate(
taste = b_Intercept + b_ModalityTaste
)Now, we can just plot the probability density of taste to get the conditional posterior probability distribution of mean emotional valence for taste words.
val_bm_draws %>%
ggplot(aes(taste)) +
geom_density(fill = "gray", alpha = 0.5) +
labs(
title = "Conditional distribution of emotional valence in taste words"
)
What if you want to show both the conditional probability of emotional valence in smell and taste words? Easy!2
val_bm_draws %>%
ggplot() +
geom_density(aes(b_Intercept), fill = "red", alpha = 0.5) +
geom_density(aes(taste), fill = "blue", alpha = 0.5) +
labs(
title = "Conditional distributions of mean emotional valence in smell vs taste words"
)
Based on this plot, we can say that the model suggests a different mean emotional valence for smell vs. taste words, and that taste words (blue) have a more positive emotional valence than smell words (red).
To reiterate from above, based on the 95% CrI of b_ModalityTaste, there is a 95% probability that the mean emotional valence of taste words is 0.18 to 0.5 greater than that of smell words.
You might ask now: Is this difference relevant? Unfortunately, statistics cannot help you to answer that question (remember, we imbue numbers with meaning). Only conceptual theories of lexical emotional valence can (or cannot)!
2.4 Reporting
You could report this model like so (text copied from above):
We fitted a Bayesian model with emotional valency as the outcome variable and modality (smell vs taste) as the only predictor. A Gaussian family was used as the outcome distribution family. The modality predictor was coded with the default treatment contrasts (with “smell” as the reference level).
According to the model, the mean valency of smell words is 5.47 (SD = 0.06), with a 95% probability that it is between 5.35 and 5.59. At 95% confidence, the difference in valency between taste and smell words is between 0.18 and 0.5 (\(\beta\) = 0.34, SD = 0.08).
The only thing that is missing is information on the conditional probability of emotional valency in taste words: you could already calculate the mean and SD of this conditional probability using the conditional posterior draws for smell words (with the summary() function, just like you would with any other data frame) and add it in the report, but we have not yet seen how to obtain Credible Intervals of conditional distributions. You will learn this in Week 7!
If you are curious about how the write-up would look like with the information on smell words, check the box below.
3 Morphological processing and reaction times
So far, we’ve seen how to work with treatment-coded variables with only two levels. In this last section, we’ll take a look at how to use dummy coding for a variable with three levels.
You might remember the shallow.csv data we used in Week 2, from Song et al. 2020. Second language users exhibit shallow morphological processing. DOI: 10.1017/S0272263120000170.
The study compared results of English L1 vs L2 participants and of left- vs right-branching words, but for this tutorial we will only be looking at the L1 and left-branching data.
The data file also contains data from the filler items, which we filter out.
Write the code to:
- Read the
shallow.csvdata. - Filter the data so that:
- You include only
L1fromGroup. - You include only
LeftfromBranching. - You include only
CriticalfromCritical_Filler. - You include only RTs that are greater than 0.
- You include only
shallow_filtThe study consisted of a lexical decision task in which participants where first shown a prime, followed by a target word for which they had to indicate whether it was a real word or a nonce word.
The prime word belonged to one of three possible groups (Releation_type in the data) each of which refers to the morphological relation of the prime and the target word:
Unrelated: for example, prolong (assuming unkindness as target, [[un-kind]-ness]).Constituent: unkind.NonConstituent: kindness.
The expectation is that lexical decision for native English participants should be facilitated in the Constituent condition, but not in the Unrelated and NonConstituent conditions (if you are curious as to why that is the expectation, I refer you to the paper).
Let’s interpret that as:
The
Constituentcondition should elicit shorter reaction times than the other two conditions.
Before moving on, let’s visualise the reaction times (RT) values, which are recorded in milliseconds (reproduce the plot by writing the code yourself).
You will notice that reaction times can only be positive: reaction time is a numeric continuous variable, bounded to positive numbers only!
Remember how we talked above about choosing an appropriate distribution to model your data? Variables that are bounded to positive numbers are known not to be distributed according to a Gaussian distribution. Each case might be a bit different, but when a variable is bounded (e.g., by zero), it is very safe to assume that the values of the variable do not follow a Gaussian distribution.3
3.1 Log-transformation
But we have a trick in our pocket: just calculate the logarithm of the values and they will conform to a Gaussian distribution! This is a common trick in psycholinguistics for modelling reaction time data.
You can calculate the logarithm (or log) of a number in R using the log() function. Calculating the logs of a variable is known as a log-transformation.
I will illustrate what this looks like with the first five values of RT in shallow.
RT <- c(603, 739, 370, 821, 1035)
RT[1] 603 739 370 821 1035log(RT)[1] 6.401917 6.605298 5.913503 6.710523 6.942157So the log of 603 is 6.4, the log of 739 is 6.6 and so on.
The shallow_filt data table already has a column with the log-transformed RTs: logRT. So let’s plot that now.
If a variable becomes Gaussian when log-transformed, the variable follows a log-normal distribution.
Now let’s look at a violin and strip plot with Relation_type on the x-axis and logRT on the y-axis.
shallow_filt %>%
ggplot(aes(Relation_type, logRT, fill = Relation_type)) +
geom_violin() +
geom_jitter(width = 0.1, alpha = 0.2)
Based on this, we can see that the bulk of the distribution (the place where the violin is wider) falls somewhat lower on the log-RT scale for the Constituent condition relative to the other conditions.
Why don’t we add the median RT in the plot? We can do so with stat_summary() (let’s also play with colour).
shallow_filt %>%
ggplot(aes(Relation_type, logRT, fill = Relation_type)) +
geom_violin() +
geom_jitter(width = 0.1, alpha = 0.2) +
stat_summary(fun = "median", colour = "#ffff99", size = 4, geom = "point") +
scale_fill_brewer(type = "qual") +
theme_black()
Check the documentation of ?stat_summary (especially the examples) if you want to learn more about it.
For the Brewer colours, see Scale brewer and for ggplot2 themes, see Complete themes.
3.2 Ordering levels of a categorical predictor
By default, the levels in Relation_type follow the order: Constituent, NonConstituent, Unrelated (this is the default alphabetical order).
There is nothing bad with this per se, but maybe we can reorder them so that they are in this order: Unrelated, NonConstituent, Constituent.
This might make a bit more sense from a conceptual point of view (you can think of the unrelated condition as the “baseline” condition).
To order levels in R, we can use a factor variable/column. We can transform the Relation_type column to a factor column with factor().
The function factor() takes the following arguments:
- The variable/column to change into a factor (here
Relation_type). - The levels in the wanted order as a string vector (here
c("Unrelated", "NonConstituent", "Constituent")), as thelevelsargument.
shallow_filt <- shallow_filt %>%
mutate(
Relation_type = factor(Relation_type, levels = c("Unrelated", "NonConstituent", "Constituent"))
)This overwrites the Relation_type column so that the levels are in the specified order. You can check this in the Environment tab: click on the blue arrow next to shallow to see the column list and now Relation_type is a Factor column with 3 levels.
3.3 Model RTs by relation type
Now it’s your turn! With the following guidance, run a model with brm() to investigate the effect of relation type on log-RT. Inspect the model summary and plot the posterior probabilities and conditional posterior probabilities.
Relation_type is a categorical variable with three levels, so here’s what treatment coding looks like (there will be N-1 = 3-1 = 2 dummy variables).
| relation_ncons | relation_cons | |
|---|---|---|
| relation = unrelated | 0 | 0 |
| relation = non-constituent | 1 | 0 |
| relation = constituent | 0 | 1 |
Here are a few formulae that describe in mathematical notation the model that we’ll need to fit. Try to go through them and unpack them based on what you’ve learned above and in the lecture.
\[ \begin{aligned} \text{logRT} &\sim Gaussian(\mu, \sigma) \\ \mu &= \beta_0 + \beta_1 \cdot relation_{ncons} + \beta_2 \cdot relation_{cons} \\ \beta_0 &\sim Gaussian(\mu_0, \sigma_0)\\ \beta_1 &\sim Gaussian(\mu_1, \sigma_1)\\ \beta_2 &\sim Gaussian(\mu_2, \sigma_2)\\ \sigma &\sim TruncGaussian(\mu_3, \sigma_3) \end{aligned} \]
Focus especially on \(\beta_0\), \(\beta_1\) and \(\beta_2\). What do they correspond to?
Fit the model with brm(). What does the R formula for this model look like?
Check the model summary. What can you say based on the estimates and the CrIs?
Plot the posterior probability distributions of \(\beta_0\), \(\beta_1\) and \(\beta_2\) and the conditional probability distributions of mean log-RTs for each relation type.
Write a report like the one above in your .Rmd file. Ask the tutors or feel free to post it on Piazza for feedback!
4 Summary
Footnotes
Note that, in real-life research contexts, you should decide which distribution to use for different outcome variables by drawing on previous research that has assessed the possible distribution families for those variables, on domain-specific expertise or common-sense. You will see that in most cases with linguistic phenomena we know very little about the nature of the variables we work with, hence it can be difficult to know which family of distributions to use.↩︎
The following code is a bit of a hack. There is a better way to do this using
pivot_longer()from the tidyr package. We will learn how to use this and other functions from that package later in the course.↩︎In fact, we have assumed above that emotional valence is distributed according to a Gaussian distribution, and we got lucky that this assumption in principle holds for the values in the data sample, because emotional valence as measured in the study is actually bounded between 1 and 8. In most cases you won’t be lucky, so always carefully think about the nature of your variables. Again, only conceptual theories of the phenomena you are investigating will be able to help.↩︎